The present embodiments relate generally to candidate classification. In particular, the present embodiments relate to classification of candidates of interest using internal correlations among the candidates.
Conventional classification methods may classify candidates of interest shown in medical images as being either normal or diseased. Conventional methods assume that the candidate samples are drawn independently and identically from an unknown data generating distribution.
In other words, typical classification methods may make the standard assumption that the data used to train and test the “classifier,” such as a candidate classification algorithm, is independently and identically distributed. For example, candidate samples may be classified one at a time in a support vector machine (SVM). As a result, the classification of a particular test sample may not depend upon the features of any other test sample. However, the standard assumption may be commonly violated in many real world applications where sub-groups of samples have a high degree of correlation amongst both their features and labels.
Examples of the problems described above involve computer aided diagnosis (CAD) applications. With CAD applications, the goal may be to assist a physician with the detection of structures of interest shown within medical images, such as identifying potentially malignant tumors in computed tomography (CT) scans or X-ray images.